Physical qubit calibration on a directed acyclic graph
arXiv:1803.03226
Abstract
High-fidelity control of qubits requires precisely tuned control parameters. Typically, these parameters are found through a series of bootstrapped calibration experiments which successively acquire more accurate information about a physical qubit. However, optimal parameters are typically different between devices and can also drift in time, which begets the need for an efficient calibration strategy. Here, we introduce a framework to understand the relationship between calibrations as a directed graph. With this approach, calibration is reduced to a graph traversal problem that is automatable and extensible.
arXiv comment7 pages, 7 figures
Cited by in corpus (7)
- Strong quantum computational advantage using a superconducting quantum processor
- Tutorial: Gate-based superconducting quantum computing
- Automated tuning of double quantum dots into specific charge states using neural networks
- Integration of spectator qubits into quantum computer architectures for hardware tuneup and calibration
- Qibolab: an open-source hybrid quantum operating system
- Robustly learning the Hamiltonian dynamics of a superconducting quantum processor
- Testing the Robustness of Robust Phase Estimation